Table 1_Revealing environmental associations underlying seasonal distribution of Pacific yellowfin tuna species with geospatial neural networks.docx

The findings indicated that: (1) sea surface temperature (temp0), temperature at 150 m depth (temp150), velocity component at 5 m depth (v5), mixed layer depth (mld), sea surface height anomaly (sla), dissolved oxygen (do), and chlorophyll concentration (chl) are seven important environmental variables; (2) GeoSpaNN achieved overall test-set values of 0.86 for R², 0.66 for root mean squared error(RMSE), and 0.40 for mean absolute error (MAE), indicating consistent predictive performance across quarters.

In addition, it more accurately reproduced the observed spatial distribution patterns; (3) Environmental interpretation of the model results indicated that temperature variables were the most important environmental factors associated with yellowfin tuna CPUE with the strongest effects observed in the western Pacific warm pool and its extension into the central Pacific. Factors such as do, mld, sla, chl, and flow velocity predominantly exert a regulatory influence in certain marine regions and during seasonal variations.

Research indicates that GeoSpaNN can concurrently delineate the nonlinear impacts and spatial dependence structure of environmental variables.</p>